Business Case Studies & Business Publications

Crawford Development Co. and Southeast B...

Ovchinnikov, Anton...

Case

Crawford Development Co. and Southeast Bank of Texas

Ovchinnikov, Anton S.; Loutskina, Elena

QA-0727 | Published December 14, 2007 | 10 pages Case

Collection: Darden School of Business

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In the early months of the 2007–08 financial crises, a loan manager faces a real estate financing decision. Should he approve a bullet structure three-year loan to a longstanding client, a legendary Texan developer? The developer, who near retirement downsized his business, is seeking financing for his only project: residential or commercial development on an attractive piece of land in suburban Houston. The loan manager considers the decision in light of the mortgage market turmoil, seeing commercial projects as safer, but also factoring that the residential market could bring higher returns if the market stabilizes soon. The manager collects the data and asks an analyst to assess the risks; that ultimately requires assessing the economics of both projects from both the bank’s and the developer’s perspectives. The bank could still change the interest rate on the loan to receive adequate compensation for the risk it carries, but the loan manager knows that doing so will change their long-term client willingness to take on the loan.

Specific objectives are as follows: 1. Discuss objectives of the lender and the borrower; 2. Conceptualize the roles of upside and downside return, expected return, down payment (collateral), and interest rate in determining the terms for the loan agreement; 3. Evaluate the lender risks in leveraged financing, specifically the borrower's risk shifting-behavior its implications; 3. Illustrate the flaw of averages (Jensen’s inequality); 4. Reinforce basic modeling skills through analysis and creation of a spreadsheet model or verification of model presented; 5. Practice decision-making under uncertainty using simulation models; 6. Master multiple regressions skills, building models with dummy variables and using the regression to provide distributional inputs into simulation models.